{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/model-based-clustering-of-zero-inflated","title":"Model-based Clustering of Multi-Dimensional Zero-Inflated Counts via the EM Algorithm","arxiv_id":"2406.00245","date":"2024-06-01","proceeding":null,"authors":["Zahra AghahosseinaliShirazi","Pedro A. Rangel","Camila P. E. de Souza"],"abstract":"Zero-inflated count data arise in various fields, including health, biology, economics, and the social sciences. These data are often modelled using probabilistic distributions such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), or zero-inflated binomial (ZIB). To account for heterogeneity in the data, it is often useful to cluster observations into groups that may explain underlying differences in the data-generating process. This paper focuses on model-based clustering for zero-inflated counts when observations are structured in a matrix form rather than a vector. We propose a clustering framework based on mixtures of ZIP or ZINB distributions, with both the count and zero components depending on cluster assignments. Our approach incorporates covariates through a log-linear structure for the mean parameter and includes a size factor to adjust for differences in total sampling or exposure. Model parameters and cluster assignments are estimated via the Expectation-Maximization (EM) algorithm. We assess the performance of our proposed methodology through simulation studies evaluating clustering accuracy and estimator properties, followed by applications to publicly available datasets.","url_abs":"https://arxiv.org/abs/2406.00245v3","url_pdf":"https://arxiv.org/pdf/2406.00245v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"model-based-clustering-of-zero-inflated","repo_url":"https://github.com/desouzalab/em-mzip","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}